#TBT

“We believe that people have the right to know when a media account is affiliated directly or indirectly with a state actor.”

– Twitter 2020

#TBT


Twitter, ca. August 2020 - March 2023

#TBT


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The “After Times” of Twitter

March 29, 2023

The “After Times” of Twitter

April 21, 2023

Who was affected by these policy shifts?


Official government and diplomatic accounts

  • Voices of the nation-state abroad
  • Highly visible regardless of labels

State-affiliated media

  • Not editorially independent from the state
  • Less attributable to the state without labels

Theoretical Expectation



Erosion of transparency policies  

⬇️  

State actors took advantage of new opportunities for authoritarian image management.  

⬇️  

They gained influence on the platform.

Comparative Analyses

🔎 Expectations: State Actor Types

State actors with high visibility will be less responsive to platform changes.

  • 🏦 Official government accounts more visible regardless of profile label.
  • 📺 State-affiliated media have more opportunities to obscure their links to state sponsorship.


🌏 Exploratory Analysis: Country

Do we observe differences in online behavior/engagement across countries?

Data

🏦📺 State Actors: Hamilton 2.0 Dashboard1

  • 1,177 accounts linked to Chinese, Iranian, and Russian state actors
  • Proxy for accounts that had government and state-affiliated media labels

Twitter Academic API via twarc CLI



Tweet timeline endpoint; up to ~3,200 tweets per account

Collected ~2.5 million tweets from 1,038 timelines

Measurement

📈 Variables

  • DV: Online activity of/with state actors
    • Engagement: Retweets, likes
    • Behavior: Tweets, topic entropy
  • IV: Policy-affected status
    • Algorithm change: Media accounts
    • Label removal: All state-affiliated media

📏 Measuring Topic Entropy

  • Shannon Entropy of topic distributions per user-day (Shannon 1948)
    • Calculated using a multilingual BERTopic model
    • Lower entropy ~ more focused discussion

💻 Method

  • Generalized Synthetic Control (Xu 2017), ITSA, RDD

Results: Actor Type

⬆️ engagement with media; 🕸️ consolidation of messaging after label removal

Daily-aggregated totals/averages

Results: Actor Type + Country

⬆️ engagement with Russian accounts; \(\Delta\) user behavior of Chinese + Russian accounts

Daily-aggregated totals/averages

Takeaways

Findings

After Twitter’s erosion of transparency policies for state actors…

  1. Notable increases in engagement with media after 2 major policy changes
  2. Messaging was (slightly) consolidated after label removal
  3. Of the three countries, Russia benefited the most from these policy changes1.


Next Steps

  • Finalizing model specs
  • Adjusting the primary approach for content analyses

Appendix

Twitter: A safe space for state influence?

 

 

Twitter’s Policy Changes in April 2023




March 29: ⬆️ engagement with prominent state media from China, Russia, Iran (Kann 2023)

April 6: “In the case of state-affiliated media entities, Twitter will not recommend or amplify accounts or their Tweets with these labels to people.”

April 12: NPR quits Twitter after being labeled as “state-affiliated media” (Folkenflik 2023)

April 21: Twitter removes all labels for “government” and “state-affiliated media”

Research Questions and Definitions

Research Questions

After Twitter’s removal of profile labels on April 21st, 2023…

  • …did engagement with state actors increase?
  • …did state actors change how they use the platform?


📖 Definitions1

  • State actor: Any individual or entity connected to government or state-affiliated media
  • Government official: Key individuals/entities representing “voices of the nation state abroad”
  • State-affiliated media: Outlets where the state exercises control over editorial content

Data on State Actors

Hamilton 2.0 Dashboard1


1,177 accounts linked to Chinese, Iranian, and Russian state actors

Proxy for accounts that had government and state-affiliated media labels

Measurement

Dependent variables

Per-day metrics 📈

  • Retweets and likes of tweets authored by state actors (H1)
  • Tweet volumes and “focus”1 of posts produced by state actors (H2)


Independent variables

  • State actor type (official government or state-affiliated media)
  • Time-related variables for parametric tests

Results

Null results at the aggregate level

Placebo Tests—Engagement and Tweet Volume

 

 

 

 

Results - Synthetic Control

All State Actors


Results - Synthetic Control

State Actor Type


Results - Synthetic Control

State Actor Type + Country


Results: Actor Type

Key result 1, Key result 2

Average per-user metrics

Results: Actor Type + Country

Key result 1, Key result 2

Average per-user metrics

References

Folkenflik, David. 2023. NPR Quits Twitter After Being Falsely Labeled as ’State-Affiliated Media’.” NPR, April.
Kann, Alyssa. 2023. “State-Controlled Media Experience Sudden Twitter Gains After Unannounced Platform Policy Change.” DFRLab.
Munger, Kevin, Richard Bonneau, Jonathan Nagler, and Joshua A. Tucker. 2019. “Elites Tweet to Get Feet Off the Streets: Measuring Regime Social Media Strategies During Protest.” Political Science Research and Methods 7 (04): 815–34. https://doi.org/10.1017/psrm.2018.3.
Schafer, Bret. 2019. “Hamilton 2.0 Methodology & FAQs.” Alliance For Securing Democracy.
Shannon, Claude Elwood. 1948. “A Mathematical Theory of Communication.” The Bell System Technical Journal 27 (3): 379–423.
Twitter. 2020. “New Labels for Government and State-Affiliated Media Accounts.” https://blog.twitter.com/en_us/topics/product/2020/new-labels-for-government-and-state-affiliated-media-accounts.
Xu, Yiqing. 2017. “Generalized Synthetic Control Method: Causal Inference with Interactive Fixed Effects Models.” Political Analysis 25 (1): 57–76. https://doi.org/10.1017/pan.2016.2.